3D ultrasound-based instrument for non-invasive measurement of amniotic fluid volume

ABSTRACT

A hand-held 3D ultrasound instrument is disclosed which is used to non-invasively and automatically measure amniotic fluid volume in the uterus requiring a minimum of operator intervention. Using a 2D image-processing algorithm, the instrument gives automatic feedback to the user about where to acquire the 3D image set. The user acquires one or more 3D data sets covering all of the amniotic fluid in the uterus and this data is then processed using an optimized 3D algorithm to output the total amniotic fluid volume corrected for any fetal head brain volume contributions.

[0001] This application is a continuation-in-part of and claims priorityto PCT application Ser. No. PCT/US03/24368 filed Aug. 1, 2003, whichclaims priority to U.S. provisional patent application serial No.60/423,881 filed Nov. 5, 2002 and U.S. provisional patent applicationserial No. 60/400,624 filed Aug. 2, 2002.

[0002] This application is also a continuation-in-part of and claimspriority to PCT Application Ser. No. PCT/US03/14785 filed May 9, 2003,which is a continuation of U.S. patent application Ser. No. 10/165,556filed Jun. 7, 2002.

[0003] This application is also a continuation-in-part of and claimspriority to U.S. patent application Ser. No. 10/633,186 filed Jul. 7,2003 which claims priority to U.S. provisional patent application serialNo. 60/423,881 filed Nov. 5, 2002 and U.S. provisional patentapplication serial No. 60/423,881 filed Aug. 2, 2002, and to U.S. patentapplication Ser. No. 10/443,126 filed May 20, 2003 which claims priorityto U.S. provisional patent application serial No. 60/423,881 filed Nov.5, 2002 and to U.S. provisional application 60/400,624 filed Aug. 2,2002.

[0004] This application also claims priority to U.S. provisional patentapplication serial No. 60/470,525 filed May 12, 2003, and to U.S. patentapplication serial No. 10/165,556 filed Jun. 7, 2002. All of the aboveapplications are herein incorporated by reference in their entirety asif fully set forth herein.

FIELD OF THE INVENTION

[0005] This invention pertains to the field of obstetrics, particularlyto ultrasound-based non-invasive obstetric measurements.

BACKGROUND OF THE INVENTION

[0006] Measurement of the amount of Amniotic Fluid (AF) volume iscritical for assessing the kidney and lung function of a fetus and alsofor assessing the placental function of the mother. Amniotic fluidvolume is also a key measure to diagnose conditions such aspolyhydramnios (too much AF) and oligohydramnios (too little AF).Polyhydramnios and oligohydramnios are diagnosed in about 7-8% of allpregnancies and these conditions are of concern because they may lead tobirth defects or to delivery complications. The amniotic fluid volume isalso one of the important components of the fetal biophysical profile, amajor indicator of fetal well-being.

[0007] The currently practiced and accepted method of quantitativelyestimating the AF volume is from two-dimensional (2D) ultrasound images.The most commonly used measure is known as the use of the amniotic fluidindex (AFI). AFI is the sum of vertical lengths of the largest AFpockets in each of the 4 quadrants. The four quadrants are defined bythe umbilicus (the navel) and the linea nigra (the vertical mid-line ofthe abdomen). The transducer head is placed on the maternal abdomenalong the longitudinal axis with the patient in the supine position Thismeasure was first proposed by Phelan et al (Phelan J P, Smith C V,Broussard P, Small M., “Amniotic fluid volume assessment with thefour-quadrant technique at 36-42 weeks' gestation,” J Reprod Med Jul;32(7): 540-2, 1987) and then recorded for a large normal population overtime by Moore and Cayle (Moore T R, Cayle J E. “The amniotic fluid indexin normal human pregnancy,” Am J Obstet Gynecol May; 162(5): 1168-73,1990).

[0008] Even though the AFI measure is routinely used, studies have showna very poor correlation of the AFI with the true AF volume (Sepulveda W,Flack N J, Fisk N M., “Direct volume measurement at midtrimesteramnioinfusion in relation to ultrasonographic indexes of amniotic fluidvolume,” Am J Obstet Gynecol Apr; 170(4): 1160-3, 1994). The correlationcoefficient was found to be as low as 0.55, even for experiencedsonographers. The use of vertical diameter only and the use of only onepocket in each quadrant are two reasons why the AFI is not a very goodmeasure of AF Volume (AFV).

[0009] Some of the other methods that have been used to estimate AFvolume include:

[0010] Dye dilution technique. This is an invasive method where a dye isinjected into the AF during amniocentesis and the final concentration ofdye is measured from a sample of AF removed after several minutes. Thistechnique is the accepted gold standard for AF volume measurement;however, it is an invasive and cumbersome method and is not routinelyused.

[0011] Subjective interpretation from ultrasound images. This techniqueis obviously dependent on observer experience and has not been found tobe very good or consistent at diagnosing oligo- or poly-hydramnios.

[0012] Vertical length of the largest single cord-free pocket. This isan earlier variation of the AFI where the diameter of only one pocket ismeasured to estimate the AF volume.

[0013] Two-diameter areas of the largest AF pockets in the fourquadrants. This is similar to the AFI; however, in this case, twodiameters are measured instead of only one for the largest pocket. Thistwo diameter area has been recently shown to be better than AFI or thesingle pocket measurement in identifying oligohydramnios (Magann E F,Perry K G Jr, Chauhan S P, Anfanger P J, Whitworth N S, Morrison J C.,“The accuracy of ultrasound evaluation of amniotic fluid volume insingleton pregnancies: the effect of operator experience and ultrasoundinterpretative technique,” J Clin Ultrasound, Jun; 25(5):249-53, 1997).

[0014] The measurement of various anatomical structures usingcomputational constructs are described, for example, in U.S Pat. No.6,346,124 to Geiser, et al. (Autonomous Boundary Detection System ForEchocardiographic Images). Similarly, the measurement of bladderstructures are covered in U.S. Pat. No. 6,213,949 to Ganguly, et al.(System For Estimating Bladder Volume) and U.S. Pat. No. 5,235,985 toMcMorrow, et al., (Automatic Bladder Scanning Apparatus). Themeasurement of fetal head structures is described in U.S. Pat. No.5,605,155 to Chalana, et al., (Ultrasound System For AutomaticallyMeasuring Fetal Head Size). The measurement of fetal weight is describedin U.S. Pat. No. 6,375,616 to Soferman, et al. (Automatic Fetal WeightDetermination).

[0015] Pertaining to ultrasound-based determination of amniotic fluidvolumes, Segiv et al. (in Segiv C, Akselrod S, Tepper R., “Applicationof a semiautomatic boundary detection algorithm for the assessment ofamniotic fluid quantity from ultrasound images.” Ultrasound Med Biol,May; 25(4): 515-26, 1999) describe a method for amniotic fluidsegmentation from 2D images. However, the Segiv et al. method isinteractive in nature and the identification of amniotic fluid volume isvery observer dependent. Moreover, the system described is not adedicated device for amniotic fluid volume assessment.

[0016] Grover et al. (Grover J, Mentakis E A, Ross M G,“Three-dimensional method for determination of amniotic fluid volume inintrauterine pockets.” Obstet Gynecol, Dec; 90(6): 1007-10, 1997)describe the use of a urinary bladder volume instrument for amnioticfluid volume measurement. The Grover et al. method makes use of thebladder volume instrument without any modifications and uses shape andother anatomical assumptions specific to the bladder that do notgeneralize to amniotic fluid pockets. Amniotic fluid pockets havingshapes not consistent with the Grover et al. bladder model introducesanalytical errors. Moreover, the bladder volume instrument does notallow for the possibility of more than one amniotic fluid pocket in oneimage scan. Therefore, the amniotic fluid volume measurements made bythe Grover et al. system may not be correct or accurate.

[0017] None of the currently used methods for AF volume estimation areideal. Therefore, there is a need for better, non-invasive, and easierways to accurately measure amniotic fluid volume.

SUMMARY OF THE INVENTION

[0018] The preferred form of the invention is a three dimensional (3D)ultrasound-based system and method using a hand-held 3D ultrasounddevice to acquire at least one 3D data set of a uterus and having aplurality of automated processes optimized to robustly locate andmeasure the volume of amniotic fluid in the uterus without resorting topre-conceived models of the shapes of amniotic fluid pockets inultrasound images. The automated process uses a plurality of algorithmsin a sequence that includes steps for image enhancement, segmentation,and polishing.

[0019] A hand-held 3D ultrasound device is used to image the uterustrans-abdominally. The user moves the device around on the maternalabdomen and, using 2D image processing to locate the amniotic fluidareas, the device gives feedback to the user about where to acquire the3D image data sets. The user acquires one or more 3D image data setscovering all of the amniotic fluid in the uterus and the data sets arethen stored in the device or transferred to a host computer.

[0020] The 3D ultrasound device is configured to acquire the 3D imagedata sets in two formats. The first format is a collection oftwo-dimensional scanplanes, each scanplane being separated from theother and representing a portion of the uterus being scanned. Eachscanplane is formed from one-dimensional ultrasound A-lines confinedwithin the limits of the 2D scanplane. The 3D data sets is thenrepresented as a 3D array of 2D scanplanes. The 3D array of 2Dscanplanes is an assembly of scanplanes, and may be assembled into atranslational array, a wedge array, or a rotatational array.

[0021] Alternatively, the 3D ultrasound device is configured to acquirethe 3D image data sets from one-dimensional ultrasound A-linesdistributed in 3D space of the uterus to form a 3D scancone of3D-distributed scanline. The 3D scancone is not an assembly of 2Dscanplanes.

[0022] The 3D image datasets, either as discrete scanplanes or 3Ddistributed scanlines, are then subjected to image enhancement andanalysis processes. The processes are either implemented on the deviceitself or is implemented on the host computer. Alternatively, theprocesses can also be implemented on a server or other computer to whichthe 3D ultrasound data sets are transferred.

[0023] In a preferred image enhancement process, each 2D image in the 3Ddataset is first enhanced using non-linear filters by an imagepre-filtering step. The image pre-filtering step includes animage-smoothing step to reduce image noise followed by animage-sharpening step to obtain maximum contrast between organ wallboundaries.

[0024] A second process includes subjecting the resulting image of thefirst process to a location method to identify initial edge pointsbetween amniotic fluid and other fetal or maternal structures. Thelocation method automatically determines the leading and trailingregions of wall locations along an A-mode one-dimensional scan line.

[0025] A third process includes subjecting the image of the firstprocess to an intensity-based segmentation process where dark pixels(representing fluid) are automatically separated from bright pixels(representing tissue and other structures).

[0026] In a fourth process, the images resulting from the second andthird step are combined to result in a single image representing likelyamniotic fluid regions.

[0027] In a fifth process, the combined image is cleaned to make theoutput image smooth and to remove extraneous structures such as thefetal head and the fetal bladder.

[0028] In a sixth process, boundary line contours are placed on each 2Dimage. Thereafter, the method then calculates the total 3D volume ofamniotic fluid in the uterus.

[0029] In cases in which uteruses are too large to fit in a single 3Darray of 2D scanplanes or a single 3D scancone of 3D distributedscanlines, especially as occurs during the second and third trimester ofpregnancy, preferred alternate embodiments of the invention allow foracquiring at least two 3D data sets, preferably four, each 3D data sethaving at least a partial ultrasonic view of the uterus, each partialview obtained from a different anatomical site of the patient.

[0030] In one embodiment a 3D array of 2D scanplanes is assembled suchthat the 3D array presents a composite image of the uterus that displaysthe amniotic fluid regions to provide the basis for calculation ofamniotic fluid volumes. In a preferred alternate embodiment, the useracquires the 3D data sets in quarter sections of the uterus when thepatient is in a supine position. In this 4-quadrant supine procedure,four image cones of data are acquired near the midpoint of each uterinequadrant at substantially equally spaced intervals between quadrantcenters. Image processing as outlined above is conducted for eachquadrant image, segmenting on the darker pixels or voxels associatedwith amniotic fluid. Correcting algorithms are applied to compensate forany quadrant-to-quadrant image cone overlap by registering and fixingone quadrant's image to another. The result is a fixed 3D mosaic imageof the uterus and the amniotic fluid volumes or regions in the uterusfrom the four separate image cones.

[0031] Similarly, in another preferred alternate embodiment, the useracquires one or more 3D image data sets of quarter sections of theuterus when the patient is in a lateral position. In this multi-imagecone lateral procedure, each image cones of data are acquired along alateral line of substantially equally spaced intervals. Each image coneare subjected to the image processing as outlined above, with emphasisgiven to segmenting on the darker pixels or voxels associated withamniotic fluid. Scanplanes showing common pixel or voxel overlaps areregistered into a common coordinate system along the lateral line.Correcting algorithms are applied to compensate for any image coneoverlap along the lateral line. The result is a fixed 3D mosaic image ofthe uterus and the amniotic fluid volumes or regions in the uterus fromthe four separate image cones.

[0032] In yet other preferred embodiments, at least two 3D scancone of3D distributed scanlines are acquired at different anatomical sites,image processed, registered and fused into a 3D mosaic image composite.Amniotic fluid volumes are then calculated.

[0033] The system and method further provides an automatic method todetect and correct for any contribution the fetal head provides to theamniotic fluid volume.

BRIEF DESCRIPTION OF THE DRAWINGS

[0034]FIG. 1 is a side view of a microprocessor-controlled, hand-heldultrasound transceiver;

[0035]FIG. 2A is a is depiction of the hand-held transceiver in use forscanning a patient;

[0036]FIG. 2B is a perspective view of the hand-held transceiver devicesitting in a communication cradle;

[0037]FIG. 3 depicts a schematic view of a plurality of transceivers inconnection with a server;

[0038]FIG. 4 depicts a schematic view of a plurality of transceivers inconnection with a server over a network;

[0039]FIG. 5A a graphical representation of a plurality of scan linesforming a single scan plane;

[0040]FIG. 5B is a graphical representation of a plurality of scanplanesforming a three-dimensional array having a substantially conic shape;

[0041]FIG. 5C is a graphical representation of a plurality of 3Ddistributed scanlines emanating from the transceiver forming a scancone;

[0042]FIG. 6 is a depiction of the hand-held transceiver placedlaterally on a patient trans-abdominally to transmit ultrasound andreceive ultrasound echoes for processing to determine amniotic fluidvolumes;

[0043]FIG. 7 shows a block diagram overview of the two-dimensional andthree-dimensional Input, Image Enhancement, Intensity-BasedSegmentation, Edge-Based Segmentation, Combine, Polish, Output, andCompute algorithms to visualize and determine the volume or area ofamniotic fluid;

[0044]FIG. 8A depicts the sub-algorithms of Image Enhancement;

[0045]FIG. 8B depicts the sub-algorithms of Intensity-BasedSegmentation;

[0046]FIG. 8C depicts the sub-algorithms of Edge-Based Segmentation;

[0047]FIG. 8D depicts the sub-algorithms of the Polish algorithm,including Close, Open, Remove Deep Regions, and Remove Fetal HeadRegions;

[0048]FIG. 8E depicts the sub-algorithms of the Remove Fetal HeadRegions sub-algorithm;

[0049]FIG. 8F depicts the sub-algorithms of the Hough Transformsub-algorithm;

[0050]FIG. 9 depicts the operation of a circular Hough transformalgorithm;

[0051]FIG. 10 shows results of sequentially applying the algorithm stepson a sample image;

[0052]FIG. 11 illustrates a set of intermediate images of the fetal headdetection process;

[0053]FIG. 12 presents a 4-panel series of sonographer amniotic fluidpocket outlines and the algorithm output amniotic fluid pocket outlines;

[0054]FIG. 13 illustrates a 4-quadrant supine procedure to acquiremultiple image cones;

[0055]FIG. 14 illustrates an in-line lateral line procedure to acquiremultiple image cones;

[0056]FIG. 15 is a block diagram overview of the rigid registration andcorrecting algorithms used in processing multiple image cone data sets;

[0057]FIG. 16 is a block diagram of the steps in the rigid registrationalgorithm;

[0058]FIG. 17A is an example image showing a first view of a fixedscanplane;

[0059]FIG. 17B is an example image showing a second view view of amoving scanplane having some voxels in common with the first scanplane;

[0060]FIG. 17C is a composite image of the first (fixed) and second(moving) images;

[0061]FIG. 18A is an example image showing a first view of a fixedscanplane;

[0062]FIG. 18B is an example image showing a second view of a movingscanplane having some voxels in common with the first view and a thirdview;

[0063]FIG. 18C is a third view of a moving scanplane having some voxelsin common with the second view; and

[0064]FIG. 18D is a composite image of the first (fixed), second(moving), and third (moving) views.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0065] The preferred portable embodiment of the ultrasound transceiverof the amniotic fluid volume measuring system are shown in FIGS. 1-4.The transceiver 10 includes a handle 12 having a trigger 14 and a topbutton 16, a transceiver housing 18 attached to the handle 12, and atransceiver dome 20. A display 24 for user interaction is attached tothe transceiver housing 18 at an end opposite the transceiver dome 20.Housed within the transceiver 10 is a single element transducer (notshown) that converts ultrasound waves to electrical signals. Thetransceiver 10 is held in position against the body of a patient by auser for image acquisition and signal processing. In operation, thetransceiver 10 transmits a radio frequency ultrasound signal atsubstantially 3.7 MHz to the body and then receives a returning echosignal. To accommodate different patients having a variable range ofobesity, the transceiver 10 can be adjusted to transmit a range ofprobing ultrasound energy from approximately 2 MHz to approximately 10MHz radio frequencies.

[0066] The top button 16 selects for different acquisition volumes. Thetransceiver is controlled by a microprocessor and software associatedwith the microprocessor and a digital signal processor of a computersystem. As used in this invention, the term “computer system” broadlycomprises any microprocessor-based or other computer system capable ofexecuting operating instructions and manipulating data, and is notlimited to a traditional desktop or notebook computer. The display 24presents alphanumeric or graphic data indicating the proper or optimalpositioning of the transceiver 10 for initiating a series of scans. Thetransceiver 10 is configured to initiate the series of scans to obtainand present 3D images as either a 3D array of 2D scanplanes or as asingle 3D scancone of 3D distributed scanlines. A suitable transceiveris the DCD372 made by Diagnostic Ultrasound. In alternate embodiments,the two- or three-dimensional image of a scan plane may be presented inthe display 24.

[0067] Although the preferred ultrasound transceiver is described above,other transceivers may also be used. For example, the transceiver neednot be battery-operated or otherwise portable, need not have atop-mounted display 24, and may include many other features ordifferences. The display 24 may be a liquid crystal display (LCD), alight emitting diode (LED), a cathode ray tube (CRT), or any suitabledisplay capable of presenting alphanumeric data or graphic images.

[0068]FIG. 2A is a photograph of the hand-held transceiver 10 forscanning a patient. The transceiver 10 is then positioned over thepatient's abdomen by a user holding the handle 12 to place thetransceiver housing 18 against the patient's abdomen. The top button 16is centrally located on the handle 12. Once optimally positioned overthe abdomen for scanning, the transceiver 10 transmits an ultrasoundsignal at substantially 3.7 MHz into the uterus. The transceiver 10receives a return ultrasound echo signal emanating from the uterus andpresents it on the display 24.

[0069]FIG. 2B is a perspective view of the hand-held transceiver devicesitting in a communication cradle. The transceiver 10 sits in acommunication cradle 42 via the handle 12. This cradle can be connectedto a standard USB port of any personal computer, enabling all the dataon the device to be transferred to the computer and enabling newprograms to be transferred into the device from the computer.

[0070]FIG. 3 depicts a schematic view of a plurality of transceivers inconnection with a server. FIG. 3, by example, depicts each transceiver10 being used to send probing ultrasound radiation to a uterus of apatient and to subsequently retrieve ultrasound echoes returning fromthe uterus, convert the ultrasound echoes into digital echo signals,store the digital echo signals, and process the digital echo signals byalgorithms of the invention. A user holds the transceiver 10 by thehandle 12 to send probing ultrasound signals and to receive incomingultrasound echoes. The transceiver 10 is placed in the communicationcradle 42 that is in signal communication with a computer 52, andoperates as an amniotic fluid volume measuring system. Two amnioticfluid volume-measuring systems are depicted as representative thoughfewer or more systems may be used. As used in this invention, a “server”can be any computer software or hardware that responds to requests orissues commands to or from a client. Likewise, the server may beaccessible by one or more client computers via the Internet, or may bein communication over a LAN or other network.

[0071] Each amniotic fluid volume measuring systems includes thetransceiver 10 for acquiring data from a patient. The transceiver 10 isplaced in the cradle 52 to establish signal communication with thecomputer 52. Signal communication as illustrated is by a wiredconnection from the cradle 42 to the computer 52. Signal communicationbetween the transceiver 10 and the computer 52 may also be by wirelessmeans, for example, infrared signals or radio frequency signals. Thewireless means of signal communication may occur between the cradle 42and the computer 52, the transceiver 10 and the computer 52, or thetransceiver 10 and the cradle 42.

[0072] A preferred first embodiment of the amniotic fluid volumemeasuring system includes each transceiver 10 being separately used on apatient and sending signals proportionate to the received and acquiredultrasound echoes to the computer 52 for storage. Residing in eachcomputer 52 are imaging programs having instructions to prepare andanalyze a plurality of one dimensional (1D) images from the storedsignals and transforms the plurality of 1D images into the plurality of2D scanplanes. The imaging programs also present 3D renderings from theplurality of 2D scanplanes. Also residing in each computer 52 areinstructions to perform the additional ultrasound image enhancementprocedures, including instructions to implement the image processingalgorithms.

[0073] A preferred second embodiment of the amniotic fluid volumemeasuring system is similar to the first embodiment, but the imagingprograms and the instructions to perform the additional ultrasoundenhancement procedures are located on the server 56. Each computer 52from each amniotic fluid volume measuring system receives the acquiredsignals from the transceiver 10 via the cradle 51 and stores the signalsin the memory of the computer 52. The computer 52 subsequently retrievesthe imaging programs and the instructions to perform the additionalultrasound enhancement procedures from the server 56. Thereafter, eachcomputer 52 prepares the 1D images, 2D images, 3D renderings, andenhanced images from the retrieved imaging and ultrasound enhancementprocedures. Results from the data analysis procedures are sent to theserver 56 for storage.

[0074] A preferred third embodiment of the amniotic fluid volumemeasuring system is similar to the first and second embodiments, but theimaging programs and the instructions to perform the additionalultrasound enhancement procedures are located on the server 56 andexecuted on the server 56. Each computer 52 from each amniotic fluidvolume measuring system receives the acquired signals from thetransceiver 10 and via the cradle 51 sends the acquired signals in thememory of the computer 52. The computer 52 subsequently sends the storedsignals to the server 56. In the server 56, the imaging programs and theinstructions to perform the additional ultrasound enhancement proceduresare executed to prepare the 1D images, 2D images, 3D renderings, andenhanced images from the server 56 stored signals. Results from the dataanalysis procedures are kept on the server 56, or alternatively, sent tothe computer 52.

[0075]FIG. 4 is a schematic view of a plurality of amniotic fluidmeasuring systems connected to a server over the Internet or othernetwork 64. FIG. 4 represents any of the first, second, or thirdembodiments of the invention advantageously deployed to other serversand computer systems through connections via the network.

[0076]FIG. 5A a graphical representation of a plurality of scan linesforming a single scan plane. FIG. 5A illustrates how ultrasound signalsare used to make analyzable images, more specifically how a series ofone-dimensional (1D) scanlines are used to produce a two-dimensional(2D) image. The 1D and 2D operational aspects of the single elementtransducer housed in the transceiver 10 is seen as it rotatesmechanically about an angle φ. A scanline 214 of length r migratesbetween a first limiting position 218 and a second limiting position 222as determined by the value of the angle φ, creating a fan-like 2Dscanplane 210. In one preferred form, the transceiver 10 operatessubstantially at 3.7 MHz frequency and creates an approximately 18 cmdeep scan line 214 and migrates within the angle φ having an angle ofapproximately 0.027 radians. A first motor tilts the transducerapproximately 60° clockwise and then counterclockwise forming thefan-like 2D scanplane presenting an approximate 120° 2D sector image. Aplurality of scanlines, each scanline substantially equivalent toscanline 214 is recorded, between the first limiting position 218 andthe second limiting position 222 formed by the unique tilt angle φ. Theplurality of scanlines between the two extremes forms a scanplane 210.In the preferred embodiment, each scanplane contains 77 scan lines,although the number of lines can vary within the scope of thisinvention. The tilt angle φ sweeps through angles approximately between−60° and +60° for a total arc of approximately 120°.

[0077]FIG. 5B is a graphical representation of a plurality of scanplanesforming a three-dimensional array (3D) 240 having a substantially conicshape. FIG. 5B illustrates how a 3D rendering is obtained from theplurality of 2D scanplanes. Within each scanplane 210 are the pluralityof scanlines, each scanline equivalent to the scanline 214 and sharing acommon rotational angle θ. In the preferred embodiment, each scanplanecontains 77 scan lines, although the number of lines can vary within thescope of this invention. Each 2D sector image scanplane 210 with tiltangle φ and range r (equivalent to the scanline 214) collectively formsa 3D conic array 240 with rotation angle θ. After gathering the 2Dsector image, a second motor rotates the transducer between 3.75° or7.5° to gather the next 120° sector image. This process is repeateduntil the transducer is rotated through 180°, resulting in thecone-shaped 3D conic array 240 data set with 24 planes rotationallyassembled in the preferred embodiment. The conic array could have feweror more planes rotationally assembled. For example, preferred alternateembodiments of the conic array could include at least two scanplanes, ora range of scanplanes from 2 to 48 scanplanes. The upper range of thescanplanes can be greater than 48 scanplanes. The tilt angle φ indicatesthe tilt of the scanline from the centerline in 2D sector image, and therotation angle θ, identifies the particular rotation plane the sectorimage lies in. Therefore, any point in this 3D data set can be isolatedusing coordinates expressed as three parameters, P(r,φ,θ).

[0078] As the scanlines are transmitted and received, the returningechoes are interpreted as analog electrical signals by a transducer,converted to digital signals by an analog-to-digital converter, andconveyed to the digital signal processor of the computer system forstorage and analysis to determine the locations of the amniotic fluidwalls. The computer system is representationally depicted in FIGS. 3 and4 and includes a microprocessor, random access memory (RAM), or othermemory for storing processing instructions and data generated by thetransceiver 10.

[0079]FIG. 5C is a graphical representation of a plurality of3D-distributed scanlines emanating from the transceiver 10 forming ascancone 300. The scancone 300 is formed by a plurality of 3Ddistributed scanlines that comprises a plurality of internal andperipheral scanlines. The scanlines are one-dimensional ultrasoundA-lines that emanate from the tranciever 10 at different coordinatedirections, that taken as an aggregate, from a conic shape. The3D-distributed A-lines (scanlines) are not necessarily confined within ascanplane, but instead are directed to sweep throughout the internal andalong the periphery of the scancone 300. The 3D-distributed scanlinesnot only would occupy a given scanplane in a 3D array of 2D scanplanes,but also the inter-scanplane spaces, from the conic axis to andincluding the conic periphery. The transceiver 10 shows the sameillustrated features from FIG. 1, but is configured to distribute theultrasound A-lines throughout 3D space in different coordinatedirections to form the scancone 300.

[0080] The internal scanlines are represented by scanlines 312A-C. Thenumber and location of the internal scanlines emanating from thetransceiver 10 is the number of internal scanlines needed to bedistributed within the scancone 300, at different positionalcoordinates, to sufficiently visualize structures or images within thescancone 300. The internal scanlines are not peripheral scanlines. Theperipheral scanlines are represented by scanlines 314A-F and occupy theconic periphery, thus representing the peripheral limits of the scancone300.

[0081]FIG. 6 is a depiction of the hand-held transceiver placed on apatient trans-abdominally to transmit probing ultrasound and receiveultrasound echoes for processing to determine amniotic fluid volumes.The transceiver 10 is held by the handle 12 to position over a patientto measure the volume of amniotic fluid in an amniotic sac over a baby.A plurality of axes for describing the orientation of the baby, theamniotic sac, and mother is illustrated. The plurality of axes includesa vertical axis depicted on the line L(R)-L(L) for left and rightorientations, a horizontal axis LI-LS for inferior and superiororientations, and a depth axis LA-LP for anterior and posteriororientations.

[0082]FIG. 6 is representative of a preferred data acquisition protocolused for amniotic fluid volume determination. In this protocol, thetransceiver 10 is the hand-held 3D ultrasound device (for example, modelDCD372 from Diagnostic Ultrasound) and is used to image the uterustrans-abdominally. Initially during the targeting phase, the patient isin a supine position and the device is operated in a 2D continuousacquisition mode. A 2D continuous mode is where the data is continuouslyacquired in 2D and presented as a scanplane similar to the scanplane 210on the display 24 while an operator physically moves the transceiver 10.An operator moves the transceiver 10 around on the maternal abdomen andthe presses the trigger 14 of the transceiver 10 and continuouslyacquires real-time feedback presented in 2D on the display 24. Amnioticfluid, where present, visually appears as dark regions along with analphanumeric indication of amniotic fluid area (for example, in cm²) onthe display 24. Based on this real-time information in terms of therelative position of the transceiver 10 to the fetus, the operatordecides which side of the uterus has more amniotic fluid by thepresentation on the display 24. The side having more amniotic fluidpresents as regions having larger darker regions on the display 24.Accordingly, the side displaying a large dark region registers greateralphanumeric area while the side with less fluid shows displays smallerdark regions and proportionately registers smaller alphanumeric area onthe display 24. While amniotic fluid is present throughout the uterus,its distribution in the uterus depends upon where and how the fetus ispositioned within the uterus. There is usually less amniotic fluidaround the fetus's spine and back and more amniotic fluid in front ofits abdomen and around the limbs.

[0083] Based on fetal position information acquired from data gatheredunder continuous acquisition mode, the patient is placed in a lateralrecumbent position such that the fetus is displaced towards the groundcreating a large pocket of amniotic fluid close to abdominal surfacewhere the transceiver 10 can be placed as shown in FIG. 6. For example,if large fluid pockets are found on the right side of the patient, thepatient is asked to turn with the left side down and if large fluidpockets are found on the left side, the patient is asked to turn withthe right side down.

[0084] After the patient has been placed in the desired position, thetransceiver 10 is again operated in the 2D continuous acquisition modeand is moved around on the lateral surface of the patient's abdomen. Theoperator finds the location that shows the largest amniotic fluid areabased on acquiring the largest dark region imaged and the largestalphanumeric value displayed on the display 24. At the lateral abdominallocation providing the largest dark region, the transceiver 10 is heldin a fixed position, the trigger 14 is released to acquire a 3D imagecomprising a set of arrayed scanplanes. The 3D image presents arotational array of the scanplanes 210 similar to the 3D array 240.

[0085] In a preferred alternate data acquisition protocol, the operatorcan reposition the transceiver 10 to different abdominal locations toacquire new 3D images comprised of different scanplane arrays similar tothe 3D array 240. Multiple scan cones obtained from different positionsprovide the operator the ability to image the entire amniotic fluidregion from different view points. In the case of a single image conebeing too small to accommodate a large AFV measurement, obtainingmultiple 3D array 240 image cones ensures that the total volume of largeAFV regions is determined. Multiple 3D images may also be acquired bypressing the top bottom 16 to select multiple conic arrays similar tothe 3D array 240.

[0086] Depending on the position of the fetus relative to the locationof the transceiver 10, a single image scan may present an underestimatedvolume of AFV due to amniotic fluid pockets that remain hidden behindthe limbs of the fetus. The hidden amniotic fluid pockets present asunquantifiable shadow-regions.

[0087] To guard against underestimating AFV, repeated positioning thetransceiver 10 and rescanning can be done to obtain more than oneultrasound view to maximize detection of amniotic fluid pockets.Repositioning and rescanning provides multiple views as a plurality ofthe 3D arrays 240 images cones. Acquiring multiple images cones improvesthe probability of obtaining initial estimates of AFV that otherwisecould remain undetected and un-quantified in a single scan.

[0088] In an alternative scan protocol, the user determines and scans atonly one location on the entire abdomen that shows the maximum amnioticfluid area while the patient is the supine position. As before, when theuser presses the top button 16, 2D scanplane images equivalent to thescanplane 210 are continuously acquired and the amniotic fluid area onevery image is automatically computed. The user selects one locationthat shows the maximum amniotic fluid area. At this location, as theuser releases the scan button, a full 3D data cone is acquired andstored in the device's memory.

[0089]FIG. 7 shows a block diagram overview the image enhancement,segmentation, and polishing algorithms of the amniotic fluid volumemeasuring system. The enhancement, segmentation, and polishingalgorithms are applied to each scanplane 210 or to the entire scan cone240 to automatically obtain amniotic fluid regions. For scanplanessubstantially equivalent to scanplane 210, the algorithms are expressedin two-dimensional terms and use formulas to convert scanplane pixels(picture elements) into area units. For the scan cones substantiallyequivalent to the 3D conic array 240, the algorithms are expressed inthree-dimensional terms and use formulas to convert voxels (volumeelements) into volume units.

[0090] The algorithms expressed in 2D terms are used during thetargeting phase where the operator trans-abdominally positions andrepositions the transceiver 10 to obtain real-time feedback about theamniotic fluid area in each scanplane. The algorithms expressed in 3Dterms are used to obtain the total amniotic fluid volume computed fromthe voxels contained within the calculated amniotic fluid regions in the3D conic array 240.

[0091]FIG. 7 represents an overview of a preferred method of theinvention and includes a sequence of algorithms, many of which havesub-algorithms described in more specific detail in FIGS. 8A-F. FIG. 7begins with inputting data of an unprocessed image at step 410. Afterunprocessed image data 410 is entered (e.g., read from memory, scanned,or otherwise acquired), it is automatically subjected to an imageenhancement algorithm 418 that reduces the noise in the data (includingspeckle noise) using one or more equations while preserving the salientedges on the image using one or more additional equations. Next, theenhanced images are segmented by two different methods whose results areeventually combined. A first segmentation method applies anintensity-based segmentation algorithm 422 that determines all pixelsthat are potentially fluid pixels based on their intensities. A secondsegmentation method applies an edge-based segmentation algorithm 438that relies on detecting the fluid and tissue interfaces. The imagesobtained by the first segmentation algorithm 422 and the images obtainedby the second segmentation algorithm 438 are brought together via acombination algorithm 442 to provide a substantially segmented image.The segmented image obtained from the combination algorithm 442 are thensubjected to a polishing algorithm 464 in which the segmented image iscleaned-up by filling gaps with pixels and removing unlikely regions.The image obtained from the polishing algorithm 464 is outputted 480 forcalculation of areas and volumes of segmented regions-of-interest.Finally the area or the volume of the segmented region-of-interest iscomputed 484 by multiplying pixels by a first resolution factor toobtain area, or voxels by a second resolution factor to obtain volume.For example, for pixels having a size of 0.8 mm by 0.8 mm, the firstresolution or conversion factor for pixel area is equivalent to 0.64mm², and the second resolution or conversion factor for voxel volume isequivalent to 0.512 mm³. Different unit lengths for pixels and voxelsmay be assigned, with a proportional change in pixel area and voxelvolume conversion factors.

[0092] The enhancement, segmentation and polishing algorithms depictedin FIG. 7 for measuring amniotic fluid areas or volumes are not limitedto scanplanes assembled into rotational arrays equivalent to the 3Darray 240. As additional examples, the enhancement, segmentation andpolishing algorithms depicted in FIG. 7 apply to translation arrays andwedge arrays. Translation arrays are substantially rectilinear imageplane slices from incrementally repositioned ultrasound transceiversthat are configured to acquire ultrasound rectilinear scanplanesseparated by regular or irregular rectilinear spaces. The translationarrays can be made from transceivers configured to advanceincrementally, or may be hand-positioned incrementally by an operator.The operator obtains a wedge array from ultrasound transceiversconfigured to acquire wedge-shaped scanplanes separated by regular orirregular angular spaces, and either mechanistically advanced orhand-tilted incrementally. Any number of scanplanes can be eithertranslationally assembled or wedge-assembled ranges, but preferably inranges greater than 2 scanplanes.

[0093] Other preferred embodiments of the enhancement, segmentation andpolishing algorithms depicted in FIG. 7 may be applied to images formedby line arrays, either spiral distributed or reconstructed random-lines.The line arrays are defined using points identified by the coordinatesexpressed by the three parameters, P(r,φ,θ), where the values or r, φ,and θ can vary.

[0094] The enhancement, segmentation and polishing algorithms depictedin FIG. 7 are not limited to ultrasound applications but may be employedin other imaging technologies utilizing scanplane arrays or individualscanplanes. For example, biological-based and non-biological-basedimages acquired using infrared, visible light, ultraviolet light,microwave, x-ray computed tomography, magnetic resonance, gamma rays,and positron emission are images suitable for the algorithms depicted inFIG. 7. Furthermore, the algorithms depicted in FIG. 7 can be applied tofacsimile transmitted images and documents.

[0095] FIGS. 8A-E depict expanded details of the preferred embodimentsof enhancement, segmentation, and polishing algorithms described in FIG.7. Each of the following greater detailed algorithms are eitherimplemented on the transceiver 10 itself or are implemented on the hostcomputer 52 or on the server 56 computer to which the ultrasound data istransferred.

[0096]FIG. 8A depicts the sub-algorithms of Image Enhancement. Thesub-algorithms include a heat filter 514 to reduce noise and a shockfilter 518 to sharpen edges. A combination of the heat and shock filtersworks very well at reducing noise and sharpening the data whilepreserving the significant discontinuities. First, the noisy signal isfiltered using a 1D heat filter (Equation E1 below), which results inthe reduction of noise and smoothing of edges. This step is followed bya shock-filtering step 518 (Equation E2 below), which results in thesharpening of the blurred signal. Noise reduction and edge sharpening isachieved by application of the following equations E1-E2. The algorithmof the heat filter 514 uses a heat equation E1. The heat equation E1 inpartial differential equation (PDE) form for image processing isexpressed as: $\begin{matrix}{{\frac{\partial u}{\partial t} = {\frac{\partial^{2}u}{\partial x^{2}} + \frac{\partial^{2}u}{\partial y^{2}}}},} & {E\quad 1}\end{matrix}$

[0097] where u is the image being processed. The image u is 2D, and iscomprised of an array of pixels arranged in rows along the x-axis, andan array of pixels arranged in columns along the y-axis. The pixelintensity of each pixel in the image u has an initial input image pixelintensity (I) defined as u₀=I. The value of I depends on theapplication, and commonly occurs within ranges consistent with theapplication. For example, I can be as low as 0 to 1, or occupy middleranges between 0 to 127 or 0 to 512. Similarly, I may have valuesoccupying higher ranges of 0 to 1024 and 0 to 4096, or greater. The heatequation E1 results in a smoothing of the image and is equivalent to theGaussian filtering of the image. The larger the number of iterationsthat it is applied for the more the input image is smoothed or blurredand the more the noise that is reduced.

[0098] The shock filter 518 is a PDE used to sharpen images as detailedbelow. The two dimensional shock filter E2 is expressed as:$\begin{matrix}{{\frac{\partial u}{\partial t} = {{- {F\left( {l(u)} \right)}}{{\nabla u}}}},} & {E\quad 2}\end{matrix}$

[0099] where u is the image processed whose initial value is the inputimage pixel intensity (D): u₀=I where the l(u) term is the Laplacian ofthe image u, F is a function of the Laplacian, and ∥∇u∥is the 2Dgradient magnitude of image intensity defined by equation E3.

∥∇u∥={square root}{square root over (u_(x) ²+u_(y) ²)},  E3

[0100] where

[0101] u² _(x)=the square of the partial derivative of the pixelintensity (u) along the x-axis,

[0102] u² _(y)=the square of the partial derivative of the pixelintensity (u) along the y-axis,

[0103] the Laplacian l(u) of the image, u, is expressed in equation E4as

l(u)=u _(xx) u _(x) ²+2u _(xy) u _(x) u _(y) +u _(yy) u _(y) ²  E4

[0104] where equation E4 relates to equation E1 as follows:

[0105] u_(x) is the first partial derivative$\frac{\partial u}{\partial x}$

[0106]  of u along the x-axis,

[0107] u_(y) is the first partial derivative$\frac{\partial u}{\partial y}$

[0108]  of u along the y-axis,

[0109] u_(x) ² is the square of the first partial derivative$\frac{\partial u}{\partial x}$

[0110]  of u along the x-axis,

[0111] u_(y) ² is the square of the first partial derivative$\frac{\partial u}{\partial y}$

[0112]  of u along the y-axis,

[0113] u_(xx) is the second partial derivative$\frac{\partial^{2}u}{\partial x^{2}}$

[0114]  of u along the x-axis,

[0115] u_(yy) is the second partial derivative$\frac{\partial^{2}u}{\partial y^{2}}$

[0116]  of u along the y-axis,

[0117] u_(xy) is cross multiple first partial derivative$\frac{\partial u}{\partial{xdy}}$

[0118]  of u along the x and y axes, and

[0119] the sign of the function F modifies the Laplacian by the imagegradient values selected to avoid placing spurious edges at points withsmall gradient values: $\begin{matrix}{{{F\left( {l(u)} \right)} = 1},{{{if}\quad {l(u)}} > {0\quad {and}\quad {{\nabla u}}} > t}} \\{{= {- 1}},{{{if}\quad {l(u)}} < {0\quad {and}\quad {{\nabla u}}} > t}} \\{{= 0},\quad {otherwise}}\end{matrix}$

[0120] otherwise

[0121] where t is a threshold on the pixel gradient value ∥∇u∥.

[0122] The combination of heat filtering and shock filtering produces anenhanced image ready to undergo the intensity-based and edge-basedsegmentation algorithms as discussed below.

[0123]FIG. 8B depicts the sub-algorithms of Intensity-Based Segmentation(step 422 in FIG. 7). The intensity-based segmentation step 422 uses a“k-means” intensity clustering 522 technique where the enhanced image issubjected to a categorizing “k-means” clustering algorithm. The“k-means” algorithm categorizes pixel intensities into white, gray, andblack pixel groups. Given the number of desired clusters or groups ofintensities (k), the k-means algorithm is an iterative algorithmcomprising four steps:

[0124] 1. Initially determine or categorize cluster boundaries bydefining a minimum and a maximum pixel intensity value for every white,gray, or black pixels into groups or k-clusters that are equally spacedin the entire intensity range.

[0125] 2. Assign each pixel to one of the white, gray or blackk-clusters based on the currently set cluster boundaries.

[0126] 3. Calculate a mean intensity for each pixel intensity k-clusteror group based on the current assignment of pixels into the differentk-clusters. The calculated mean intensity is defined as a clustercenter. Thereafter, new cluster boundaries are determined as mid pointsbetween cluster centers.

[0127] 4. Determine if the cluster boundaries significantly changelocations from their previous values. Should the cluster boundarieschange significantly from their previous values, iterate back to step 2,until the cluster centers do not change significantly betweeniterations. Visually, the clustering process is manifest by thesegmented image and repeated iterations continue until the segmentedimage does not change between the iterations.

[0128] The pixels in the cluster having the lowest intensity value—thedarkest cluster—are defined as pixels associated with amniotic fluid.For the 2D algorithm, each image is clustered independently of theneighboring images. For the 3D algorithm, the entire volume is clusteredtogether. To make this step faster, pixels are sampled at 2 or anymultiple sampling rate factors before determining the clusterboundaries. The cluster boundaries determined from the down-sampled dataare then applied to the entire data.

[0129]FIG. 8C depicts the sub-algorithms of Edge-Based Segmentation(step 438 in FIG. 7) and uses a sequence of four sub-algorithms. Thesequence includes a spatial gradients 526 algorithm, a hysteresisthreshold 530 algorithm, a Region-of-Interest (ROI) 534 algorithm, and amatching edges filter 538 algorithm.

[0130] The spatial gradient 526 computes the x-directional andy-directional spatial gradients of the enhanced image. The Hysteresisthreshold 530 algorithm detects salient edges. Once the edges aredetected, the regions defined by the edges are selected by a useremploying the ROI 534 algorithm to select regions-of-interest deemedrelevant for analysis.

[0131] Since the enhanced image has very sharp transitions, the edgepoints can be easily determined by taking x- and y- derivatives usingbackward differences along x- and y-directions. The pixel gradientmagnitude ∥∇I∥ is then computed from the x- and y-derivative image inequation E5 as:

∥∇I∥={square root}{square root over (I_(x) ²+I_(y) ²)}  E5

[0132] Where I² _(x)=the square of x-derivative of intensity; and

[0133] I² _(y)=the square of y-derivative of intensity along the y-axis.

[0134] Significant edge points are then determined by thresholding thegradient magnitudes using a hysteresis thresholding operation. Otherthresholding methods could also be used. In hysteresis thresholding 530,two threshold values, a lower threshold and a higher threshold, areused. First, the image is thresholded at the lower threshold value and aconnected component labeling is carried out on the resulting image.Next, each connected edge component is preserved which has at least oneedge pixel having a gradient magnitude greater than the upper threshold.This kind of thresholding scheme is good at retaining long connectededges that have one or more high gradient points.

[0135] In the preferred embodiment, the two thresholds are automaticallyestimated. The upper gradient threshold is estimated at a value suchthat at most 97% of the image pixels are marked as non-edges. The lowerthreshold is set at 50% of the value of the upper threshold. Thesepercentages could be different in different implementations. Next, edgepoints that lie within a desired region-of-interest are selected 534.This region of interest selection 534 excludes points lying at the imageboundaries and points lying too close to or too far from the transceiver10. Finally, the matching edge filter 538 is applied to remove outlieredge points and fill in the area between the matching edge points.

[0136] The edge-matching algorithm 538 is applied to establish validboundary edges and remove spurious edges while filling the regionsbetween boundary edges. Edge points on an image have a directionalcomponent indicating the direction of the gradient. Pixels in scanlinescrossing a boundary edge location will exhibit two gradient transitionsdepending on the pixel intensity directionality. Each gradienttransition is given a positive or negative value depending on the pixelintensity directionality. For example, if the scanline approaches anecho reflective bright wall from a darker region, then an ascendingtransition is established as the pixel intensity gradient increases to amaximum value, i.e., as the transition ascends from a dark region to abright region. The ascending transition is given a positive numericalvalue. Similarly, as the scanline recedes from the echo reflective wall,a descending transition is established as the pixel intensity gradientdecreases to or approaches a minimum value. The descending transition isgiven a negative numerical value.

[0137] Valid boundary edges are those that exhibit ascending anddescending pixel intensity gradients, or equivalently, exhibit paired ormatched positive and negative numerical values. The valid boundary edgesare retained in the image Spurious or invalid boundary edges do notexhibit paired ascending-descending pixel intensity gradients, i.e., donot exhibit paired or matched positive and negative numerical values.The spurious boundary edges are removed from the image.

[0138] For amniotic fluid volume related applications, most edge pointsfor amniotic fluid surround a dark, closed region, with directionspointing inwards towards the center of the region. Thus, for aconvex-shaped region, the direction of a gradient for any edge point,the edge point having a gradient direction approximately opposite to thecurrent point represents the matching edge point. Those edge pointsexhibiting an assigned positive and negative value are kept as validedge points on the image because the negative value is paired with itspositive value counterpart. Similarly, those edge point candidateshaving unmatched values, i.e., those edge point candidates not having anegative-positive value pair, are deemed not to be true or valid edgepoints and are discarded from the image.

[0139] The matching edge point algorithm 538 delineates edge points notlying on the boundary for removal from the desired dark regions.Thereafter, the region between any two matching edge points is filled inwith non-zero pixels to establish edge-based segmentation. In apreferred embodiment of the invention, only edge points whose directionsare primarily oriented co-linearly with the scanline are sought topermit the detection of matching front wall and back wall pairs.

[0140] Returning to FIG. 7, once Intensity-Based 422 and Edge-BasedSegmentation 438 is completed, both segmentation methods use a combiningstep that combines the results of intensity-based segmentation 422 stepand the edge-based segmentation 438 step using an AND Operator of Images442. The AND Operator of Images 442 is achieved by a pixel-wise BooleanAND operator 442 step to produce a segmented image by computing thepixel intersection of two images. The Boolean AND operation 442represents the pixels as binary numbers and the corresponding assignmentof an assigned intersection value as a binary number 1 or 0 by thecombination of any two pixels. For example, consider any two pixels, saypixel_(A) and pixel_(B), which can have a 1 or 0 as assigned values. Ifpixel_(A)'s value is 1, and pixel_(B)'s value is 1, the assignedintersection value of pixel_(A) and pixel_(B) is 1. If the binary valueof pixel_(A) and pixel_(B) are both 0, or if either pixel_(A) orpixel_(B) is 0, then the assigned intersection value of pixel_(A) andpixel_(B) is 0. The Boolean AND operation 542 takes the binary any twodigital images as input, and outputs a third image with the pixel valuesmade equivalent to the intersection of the two input images.

[0141] Upon completion of the AND Operator of Images 442 algorithm, thepolish 464 algorithm of FIG. 7 is comprised of multiple sub-algorithms.FIG. 8D depicts the sub-algorithms of the Polish 464 algorithm,including a Close 546 algorithm, an Open 550 algorithm, a Remove DeepRegions 554 algorithm, and a Remove Fetal Head Regions 560 algorithm.

[0142] Closing and opening algorithms are operations that process imagesbased on the knowledge of the shape of objects contained on a black andwhite image, where white represents foreground regions and blackrepresents background regions. Closing serves to remove backgroundfeatures on the image that are smaller than a specified size. Openingserves to remove foreground features on the image that are smaller thana specified size. The size of the features to be removed is specified asan input to these operations. The opening algorithm 550 removes unlikelyamniotic fluid regions from the segmented image based on a-prioriknowledge of the size and location of amniotic fluid pockets.

[0143] Referring to FIG. 8D, the closing 546 algorithm obtains theApparent Amniotic Fluid Area (AAFA) or Volume (AAFV) values. The AAFAand AAFV values are “Apparent” and maximal because these values maycontain region areas or region volumes of non-amniotic originunknowingly contributing to and obscuring what otherwise would be thetrue amniotic fluid volume. For example, the AAFA and AAFV valuescontain the true amniotic volumes, and possibly as well areas or volumesdue to deep tissues and undetected fetal head volumes. Thus the apparentarea and volume values require correction or adjustments due to unknowncontributions of deep tissue and of the fetal head in order to determinean Adjusted Amniotic Fluid Area (AdAFA) value or Volume (AdAVA) value568.

[0144] The AdAFA and AdAVA values obtained by the Close 546 algorithmare reduced by the morphological opening algorithm 550. Thereafter, theAdAFA and ADAVA values are further reduced by removing areas and volumesattributable to deep regions by using the Remove Deep Regions 554algorithm. Thereafter, the polishing algorithm 464 continues by applyinga fetal head region detection algorithm 560.

[0145]FIG. 8E depicts the sub-algorithms of the Remove Fetal HeadRegions sub-algorithm 560. The basic idea of the sub-algorithms of thefetal head detection algorithm 560 is that the edge points thatpotentially represent a fetal skull are detected. Thereafter, a circlefinding algorithm to determine the best-fitting circle to these fetalskull edges is implemented. The radii of the circles that are searchedare known a priori based on the fetus' gestational age. The best fittingcircle whose fitting metric lies above a certain pre-specified thresholdis marked as the fetal head and the region inside this circle is thefetal head region. The algorithms include a gestational Age 726 input, adetermine head diameter factor 730 algorithm, a Head Edge Detectionalgorithm, 734, and a Hough transform procedure 736.

[0146] Fetal brain tissue has substantially similar ultrasound echoqualities as presented by amniotic fluid. If not detected and subtractedfrom amniotic fluid volumes, fetal brain tissue volumes will be measuredas part of the total amniotic fluid volumes and lead to anoverestimation and false diagnosis of oligo or poly-hyraminoticconditions. Thus detecting fetal head position, measuring fetal brainmatter volumes, and deducting the fetal brain matter volumes from theamniotic fluid volumes to obtain a corrected amniotic fluid volumeserves to establish accurately measure amniotic fluid volumes.

[0147] The gestational age input 726 begins the fetal head detectionalgorithm 560 and uses a head dimension table to obtain ranges of headbi-parietal diameters (BPD) to search for (e.g., 30 week gestational agecorresponds to a 6 cm head diameter). The head diameter range is inputto both the Head Edge Detection, 734, and the Hough Transform, 736., Thehead edge detection 734 algorithm seeks out the distinctively brightultrasound echoes from the anterior and posterior walls of the fetalskull while the Hough Transform algorithm, 736, finds the fetal headusing circular shapes as models for the fetal head in the Cartesianimage (pre-scan conversion to polar form).

[0148] Scanplanes processed by steps 522, 538, 530, are input to thehead edge detection step 734. Applied as the first step in the fetalhead detection algorithm 734 is the detection of the potential headedges from among the edges found by the matching edge filter. Thematching edge 538 filter outputs pairs of edge points potentiallybelonging to front walls or back walls. Not all of these wallscorrespond to fetal head locations. The edge points representing thefetal head are determined using the following heuristics:

[0149] (1) Looking along a one dimensional A-mode scan line, fetal headlocations present a corresponding matching gradient in the opposingdirection within a short distance approximately the same size as thethickness of the fetal skull. This distance is currently set to a value1 cm.

[0150] (2) The front wall and the back wall locations of the fetal headare within a range of diameters corresponding to the expected diameter730 for the gestational age 726 of the fetus. Walls that are too closeor too far are not likely to be head locations.

[0151] (3) A majority of the pixels between the front and back walllocations of the fetal head lie within the minimum intensity cluster asdefined by the output of the clustering algorithm 422. The percentage ofpixels that need to be dark is currently defined to be 80%.

[0152] The pixels found satisfying these features are then verticallydilated to produce a set of thick fetal head edges as the output of HeadEdge Detection, 734.

[0153]FIG. 8F depicts the sub-algorithms of the Hough transformprocedure 736. The sub-algorithms include a Polar Hough Transform 738algorithm, a find maximum Hough value 742 algorithm 742, and a fillcircle region 746. The Polar Hough Transform algorithm looks for fetalhead structures in polar coordinate terms by converting from Cartesiancoordinates using a plurality of equations. The fetal head, whichappears like a circle in a 3D scan-converted Cartesian coordinate image,has a different shape in the pre-scan converted polar space. The fetalhead shape is expressed in terms of polar coordinate terms explained asfollows:

[0154] The coordinates of a circle in the Cartesian space (x,y) withcenter (x₀, y₀) and radius R are defined for an angle θ are derived anddefined in equation E5 as:

x=R cos θ+x ₀

y=R sin θ+y ₀

(x−x ₀)²+(y−y ₀)² =R ²  E5

[0155] In polar space, the coordinates (r,φ), with respect to the center(r₀,φ₀), are derived and defined in equation E6 as:

r sin φ=R cos θ+r ₀ sin φ₀

r cos φ=R sin θ+r ₀ cos φ₀

(r sin φ−r ₀ sin φ₀)²+(r cos φ−r ₀ cos φ₀)² =R ²  E6

[0156] The Hough transform 736 algorithm using equations E5 and E6attempts to find the best-fit circle to the edges of an image. A circlein the polar space is defined by a set of three parameters, (r₀,φ₀,R)representing the center and the radius of the circle.

[0157] The basic idea for the Hough transform 736 is as follows. Supposea circle is sought having a fixed radius (say, R1) for which the bestcenter of the circle is similarly sought. Now, every edge point on theinput image lies on a potential circle whose center lays R1 pixels awayfrom it. The set of potential centers themselves form a circle of radiusR1 around each edge pixel. Now, drawing potential circles of radius R1around each edge pixel, the point at which most circles intersect, acenter of the circle that represents a best-fit circle to the given edgepoints is obtained. Therefore, each pixel in the Hough transform outputcontains a likelihood value that is simply the count of the number ofcircles passing through that point.

[0158]FIG. 9 illustrates the Hough Transform 736 algorithm for aplurality of circles with a fixed radius in a Cartesian coordinatesystem. A portion of the plurality of circles is represented by a firstcircle 804 a, a second circle 804 b, and a third circle 804 c. Aplurality of edge pixels are represented as gray squares and an edgepixel 808 is shown. A circle is drawn around each edge pixel todistinguish a center location 812 of a best-fit circle 816 passingthrough each edge pixel point; the point of the center location throughwhich most such circles pass (shown by a gray star 812) is the center ofthe best-fit circle 816 presented as a thick dark line. Thecircumference of the best fit circle 816 passes substantially through iscentral portion of each edge pixel, represented as a series of squaressubstantially equivalent to the edge pixel 808.

[0159] This search for best fitting circles can be easily extended tocircles with varying radii by adding one more degree of freedom—however,a discrete set of radii around the mean radii for a given gestationalage makes the search significantly faster, as it is not necessary tosearch all possible radii.

[0160] The next step in the head detection algorithm is selecting orrejecting best-fit circles based on its likelihood, in the find maximumHough Value 742 algorithm. The greater the number of circles passingthrough a given point in the Hough-space, the more likely it is to bethe center of a best-fit circle. A 2D metric as a maximum Hough value742 of the Hough transform 736 output is defined for every image in adataset. The 3D metric is defined as the maximum of the 2D metrics forthe entire 3D dataset. A fetal head is selected on an image depending onwhether its 3D metric value exceeds a preset 3D threshold and alsowhether the 2D metric exceeds a preset 2D threshold. The 3D threshold iscurrently set at 7 and the 2D threshold is currently set at 5. Thesethresholds have been determined by extensive training on images wherethe fetal head was known to be present or absent.

[0161] Thereafter, the fetal head detection algorithm concludes with afill circle region 746 that incorporates pixels to the image within thedetected circle. The fill circle region 746 algorithm fills the insideof the best fitting polar circle. Accordingly, the fill circle region746 algorithm encloses and defines the area of the fetal brain tissue,permitting the area and volume to be calculated and deducted viaalgorithm 554 from the apparent amniotic fluid area and volume (AAFA orAAFV) to obtain a computation of the corrected amniotic fluid area orvolume via algorithm 484.

[0162]FIG. 10 shows the results of sequentially applying the algorithmsteps of FIGS. 7 and 8A-D on an unprocessed sample image 820 presentedwithin the confines of a scanplane substantially equivalent to thescanplane 210. The results of applying the heat filter 514 and shockfilter 518 in enhancing the unprocessed sample is shown in enhancedimage 840. The result of intensity-based segmentation algorithms 522 isshown in image 850. The results of edge-based segmentation 438 algorithmusing sub-algorithms 526, 530, 534 and 538 of the enhanced image 840 isshown in segmented image 858. The result of the combination 442utilizing the Boolean AND images 442 algorithm is shown in image 862where white represents the amniotic fluid area. The result of applyingthe polishing 464 algorithm employing algorithms 542, 546, 550, 554,560, and 564 is shown in image 864, which depicts the amniotic fluidarea overlaid on the unprocessed sample image 810.

[0163]FIG. 11 depicts a series of images showing the results of theabove method to automatically detect, locate, and measure the area andvolume of a fetal head using the algorithms outlined in FIGS. 7 and8A-F. Beginning with an input image in polar coordinate form 920, thefetal head image is marked by distinctive bright echoes from theanterior and posterior walls of the fetal skull and a circular shape ofthe fetal head in the Cartesian image. The fetal head detectionalgorithm 734 operates on the polar coordinate data (i.e., pre-scanversion, not yet converted to Cartesian coordinates).

[0164] An example output of applying the head edge detection 734algorithm to detect potential head edges is shown in image 930.Occupying the space between the anterior and posterior walls are dilatedblack pixels 932 (stacks or short lines of black pixels representingthick edges). An example of the polar Hough transform 738 for one actualdata sample for a specific radius is shown in polar coordinate image940.

[0165] An example of the best-fit circle on real data polar data isshown in polar coordinate image 950 that has undergone the find maximumHough value step 742. The polar coordinate image 950 is scan-convertedto a Cartesian data in image 960 where the effects of finding maximumHough value 742 algorithm are seen in Cartesian format.

[0166]FIG. 12 presents a 4-panel series of sonographer amniotic fluidpocket outlines compared to the algorithm's output in a scanplaneequivalent to scanplane 210. The top two panels depict the sonographer'soutlines of amniotic fluid pockets obtained by manual interactions withthe display while the bottom two panels show the resulting amnioticfluid boundaries obtained from the instant invention's automaticapplication of 2D algorithms, 3D algorithms, combination heat and shockfilter algorithms, and segmentation algorithms.

[0167] After the contours on all the images have been delineated, thevolume of the segmented structure is computed. Two specific techniquesfor doing so are disclosed in detail in U.S. Pat. No. 5,235,985 toMcMorrow et al, herein incorporated by reference. This patent providesdetailed explanations for non-invasively transmitting, receiving andprocessing ultrasound for calculating volumes of anatomical structures.

[0168] Multiple Image Cone Acquisition and Image Processing Procedures:

[0169] In some embodiments, multiple cones of data acquired at multipleanatomical sampling sites may be advantageous. For example, in someinstances, the pregnant uterus may be too large to completely fit in onecone of data sampled from a single measurement or anatomical site of thepatient (patient location). That is, the transceiver 10 is moved todifferent anatomical locations of the patient to obtain different 3Dviews of the uterus from each measurement or transceiver location.

[0170] Obtaining multiple 3D views may be especially needed during thethird trimester of pregnancy, or when twins or triplets are involved. Insuch cases, multiple data cones can be sampled from different anatomicalsites at known intervals and then combined into a composite image mosaicto present a large uterus in one, continuous image. In order to make acomposite image mosaic that is anatomically accurate without duplicatingthe anatomical regions mutually viewed by adjacent data cones,ordinarily it is advantageous to obtain images from adjacent data conesand then register and subsequently fuse them together. hi a preferredembodiment, to acquire and process multiple 3D data sets or imagescones, at least two 3D image cones are generally preferred, with oneimage cone defined as fixed, and the other image cone defined as moving.

[0171] The 3D image cones obtained from each anatomical site may be inthe form of 3D arrays of 2D scanplanes, similar to the 3D array 240.Furthermore, the 3D image cone may be in the form of a wedge or atranslational array of 2D scanplanes. Alternatively, the 3D image coneobtained from each anatomical site may be a 3D scancone of3D-distributed scanlines, similar to the scancone 300.

[0172] The term “registration” with reference to digital images meansthe determination of a geometrical transformation or mapping that alignsviewpoint pixels or voxels from one data cone sample of the object (inthis embodiment, the uterus) with viewpoint pixels or voxels fromanother data cone sampled at a different location from the object. Thatis, registration involves mathematically determining and converting thecoordinates of common regions of an object from one viewpoint to thecoordinates of another viewpoint. After registration of at least twodata cones to a common coordinate system, the registered data coneimages are then fused together by combining the two registered dataimages by producing a reoriented version from the view of one of theregistered data cones. That is, for example, a second data cone's viewis merged into a first data cone's view by translating and rotating thepixels of the second data cone's pixels that are common with the pixelsof the first data cone. Knowing how much to translate and rotate thesecond data cone's common pixels or voxels allows the pixels or voxelsin common between both data cones to be superimposed into approximatelythe same x, y, z, spatial coordinates so as to accurately portray theobject being imaged. The more precise and accurate the pixel or voxelrotation and translation, the more precise and accurate is the commonpixel or voxel superimposition or overlap between adjacent image cones.The precise and accurate overlap between the images assures theconstruction of an anatomically correct composite image mosaicsubstantially devoid of duplicated anatomical regions.

[0173] To obtain the precise and accurate overlap of common pixels orvoxels between the adjacent data cones, it is advantageous to utilize ageometrical transformation that substantially preserves most or alldistances regarding line straightness, surface planarity, and anglesbetween the lines as defined by the image pixels or voxels. That is, thepreferred geometrical transformation that fosters obtaining ananatomically accurate mosaic image is a rigid transformation thatdoesn't permit the distortion or deforming of the geometrical parametersor coordinates between the pixels or voxels common to both image cones.

[0174] The preferred rigid transformation first converts the polarcoordinate scanplanes from adjacent image cones into in x, y, zCartesian axes. After converting the scanplanes into the Cartesiansystem, a rigid transformation, T, is determined from the scanplanes ofadjacent image cones having pixels in common. The transformation T is acombination of a three-dimensional translation vector expressed inCartesian as t=(T_(x), T_(y), T_(z),), and a three-dimensional rotationR matrix expressed as a function of Euler angles θ_(x), θ_(y), θ_(z)around the x, y, and z axes. The transformation represents a shift androtation conversion factor that aligns and overlaps common pixels fromthe scanplanes of the adjacent image cones.

[0175] In the preferred embodiment of the present invention, the commonpixels used for the purposes of establishing registration ofthree-dimensional images are the boundaries of the amniotic fluidregions as determined by the amniotic fluid segmentation algorithmdescribed above.

[0176] Several different protocols may be used to collect and processmultiple cones of data from more than one measurement site are describedin FIGS. 13-14.

[0177]FIG. 13 illustrates a 4-quadrant supine procedure to acquiremultiple image cones around the center point of uterine quadrants of apatient in a supine procedure. Here the patient lies supine (on herback) displacing most or all of the amniotic fluid towards the top. Theuterus is divided into 4 quadrants defined by the umbilicus (the navel)and the linea-nigra (the vertical center line of the abdomen) and asingle 3D scan is acquired at each quadrant. The 4-quadrant supineprotocol acquires four different 3D scans in a two dimensional grid,each corner of the grid being a quadrant midpoint. Four cones of dataare acquired by the transceiver 10 along the midpoints of quadrant 1,quadrant 2, quadrant 3, and quadrant 4. Thus, one 3D data cone peruterine quadrant midpoint is acquired such that each quadrant midpointis mutually substantially equally spaced from each other in afour-corner grid array.

[0178]FIG. 14 illustrates a multiple lateral line procedure to acquiremultiple image cones in a linear array. Here the patent lies laterally(on her side), displacing most or all of the amniotic fluid towards thetop. Four 3D images cones of data are acquired along a line ofsubstantially equally space intervals. As illustrated, the transceiver10 moves along the lateral line at position 1, position 2, position 3,and position 4. As illustrated in FIG. 14, the inter-position distanceor interval is approximately 6 cm.

[0179] The preferred embodiment for making a composite image mosaicinvolves obtaining four multiple image cones where the transceiver 10 isplaced at four measurement sites over the patient in a supine or lateralposition such that at least a portion of the uterus is ultrasonicallyviewable at each measurement site. The first measurement site isoriginally defined as fixed, and the second site is defined as movingand placed at a first known inter-site distance relative to the firstsite. The second site images are registered and fused to the first siteimages After fusing the second site images to the first site images, thethird measurement site is defined as moving and placed at a second knowninter-site distance relative to the fused second site now defined asfixed. The third site images are registered and fused to the second siteimages Similarly, after fusing the third site images to the second siteimages, the fourth measurement site is defined as moving and placed at athird known inter-site distance relative to the fused third site nowdefined as fixed. The fourth site images are registered and fused to thethird site images

[0180] The four measurement sites may be along a line or in an array.The array may include rectangles, squares, diamond patterns, or othershapes. Preferably, the patient is positioned such that the baby movesdownward with gravity in the uterus and displaces the amniotic fluidupwards toward the measuring positions of the transceiver 10.

[0181] The interval or distance between each measurement site isapproximately equal, or may be unequal. For example in the lateralprotocol, the second site is spaced approximately 6 cm from the firstsite, the third site is spaced approximately 6 cm from the second site,and the fourth site is spaced approximately 6 cm from the third site.The spacing for unequal intervals could be, for example, the second siteis spaced approximately 4 cm from the first site, the third site isspaced approximately 8 cm from the second site, and the third is spacedapproximately 6 cm from the third site. The interval distance betweenmeasurement sites may be varied as long as there are mutually viewableregions of portions of the uterus between adjacent measurement sites.

[0182] For uteruses not as large as requiring four measurement sites,two and three measurement sites may be sufficient for making a composite3D image mosaic. For three measurement sites, a triangular array ispossible, with equal or unequal intervals. Furthermore, is the case whenthe second and third measurement sites have mutually viewable regionsfrom the first measurement site, the second interval may be measuredfrom the first measurement site instead of measuring from the secondmeasurement site.

[0183] For very large uteruses not fully captured by four measurement oranatomical sites, greater than four measurement sites may be used tomake a composite 3D image mosaic provided that each measurement site isultrasonically viewable for at least a portion of the uterus. For fivemeasurement sites, a pentagon array is possible, with equal or unequalintervals. Similarly, for six measurement sites, a hexagon array ispossible, with equal or unequal intervals between each measurement site.Other polygonal arrays are possible with increasing numbers ofmeasurement sites.

[0184] The geometrical relationship between each image cone must beascertained so that overlapping regions can be identified between anytwo image cones to permit the combining of adjacent neighboring cones sothat a single 3D mosaic composite image is produced from the 4-quadrantor in-line laterally acquired images.

[0185] The translational and rotational adjustments of each moving coneto conform with the voxels common to the stationary image cone is guidedby an inputted initial transform that has the expected translational androtational values. The distance separating the transceiver 10 betweenimage cone acquisitions predicts the expected translational androtational values. For example, as shown in FIG. 14, if 6 cm separatesthe image cones, then the expected translational and rotational valuesare proportionally estimated. For example, the (T_(x), T_(y), T_(z)) and(θ_(x), θ_(y), θ_(z)) Cartesian and Euler angle terms fixed images pvoxel values are defined respectively as (6 cm, 0 cm, 0 cm) and (0 deg,0 deg, 0 deg).

[0186]FIG. 15 is a block diagram algorithm overview of the registrationand correcting algorithms used in processing multiple image cone datasets. The algorithm overview 1000 shows how the entire amniotic fluidvolume measurement process occurs from the multiply acquired imagecones. First, each of the input cones 1004 is segmented 1008 to detectall amniotic fluid regions. The segmentation 1008 step is substantiallysimilar to steps 418-480 of FIG. 7. Next, these segmented regions areused to align (register) the different cones into one common coordinatesystem using a Rigid Registration 1012 algorithm. Next, the registereddatasets from each image cone are fused with each other using a FuseData 1016 algorithm to produce a composite 3D mosaic image. Thereafter,the total amniotic fluid volume is computed 1020 from the fused orcomposite 3D mosaic image.

[0187]FIG. 16 is a block diagram of the steps of the rigid registrationalgorithm 1012. The rigid algorithm 1012 is a 3D image registrationalgorithm and is a modification of the Iterated Closest Point (ICP)algorithm published by P J Besl and N D McKay, in “A Method forRegistration of 3-D Shapes,” IEEE Trans. Pattern Analysis & MachineIntelligence, vol. 14, no. 2, Feb. 1992, pp. 239-256. The steps of therigid registration algorithm 1012 serves to correct for overlap betweenadjacent 3D scan cones acquired in either the 4-quadrant supine gridprocedure or lateral line multi data cone acquisition procedures. Therigid algorithm 1012 first processes the fixed image 1104 in polarcoordinate terms to Cartesian coordinate terms using the 3D Scan Convert1108 algorithm. Separately, the moving image 1124 is also converted toCartesian coordinates using the 3D Scan Convert 1128 algorithm. Next,the edges of the amniotic fluid regions on the fixed and moving imagesare determined and converted into point sets p and q respectively by a3D edge detection process 1112 and 1132. Also, the fixed image pointset, p, undergoes a 3D distance transform process 1116 which maps everyvoxel in a 3D image to a number representing the distance to the closestedge point in p. Pre-computing this distance transform makes subsequentdistance calculations and closest point determinations very efficient.

[0188] Next, the known initial transform 1136, for example, (6, 0, 0)for the Cartesian T_(x), T_(y), T_(z) terms and (0, 0, 0) for theθ_(x),θ_(y), θ_(z) Euler angle terms for an inter-transceiver interval of 6cm, is subsequently applied to the moving image by the Apply Transform1140 step. This transformed image is then compared to the fixed image toexamine for the quantitative occurrence of overlapping voxels. If theoverlap is less than 20%, there are not enough common voxels availablefor registration and the initial transform is considered sufficient forfusing at step 1016.

[0189] If the overlapping voxel sets by the initial transform exceed 20%of the fixed image p voxel sets, the q-voxels of the initial transformare subjected to an iterative sequence of rigid registration.

[0190] A transformation T serves to register a first voxel point set pfrom the first image cone by merging or overlapping a second voxel pointset q from a second image cone that is common to p of the first imagecone. A point in the first voxel point set p may be defined asp_(i)=(x_(i), y_(i), z_(i)) and a point in the second voxel point set qmay similarly be defined as q_(j)=(x_(j), y_(j), z_(j)), If the firstimage cone is considered to be a fixed landmark, then the T factor isapplied to align (translate and rotate) the moving voxel point set qonto the fixed voxel point set p.

[0191] The precision of T is often affected by noise in the images thataccordingly affects the precision of t and R, and so the variability ofeach voxel point set will in turn affect the overall variability of eachmatrix equation set for each point. The composite variability betweenthe fixed voxel point set p and a corresponding moving voxel point set qis defined to have a cross-covariance matrix C_(pq), more fullydescribed in equation E8 as: $\begin{matrix}{C_{pq} = {\frac{1}{n}{\sum\limits_{i = {1\quad \ldots \quad n}}\quad {\left( {p_{i} - \overset{\_}{p}} \right)\left( {q_{i} - \overset{\_}{q}} \right)^{T}}}}} & {E8}\end{matrix}$

[0192] where, n is the number of points in each point set and {overscore(p)} and {overscore (q)} are the central points in the two voxel pointsets. How strong the correlation is between two sets data is determinedby statistically analyzing the cross-covariance C_(pq). The preferredembodiment uses a statistical process known as the Single ValueDecomposition (SVD) originally developed by Eckart and Young (G. Eckartand G. Young, 1936, The Approximation of One Matrix by Another of LowerRank, Pychometrika 1, 211-218). When numerical data is organized intomatrix form, the SVD is applied to the matrix, and the resulting SVDvalues are determined to solve for the best fitting rotation transform Rto be applied to the moving voxel point set q to align with the fixedvoxel point set p to acquire optimum overlapping accuracy of the pixelor voxels common to the fixed and moving images.

[0193] Equation E9 gives the SVD value of the cross-covariance C_(pq):

C_(pq)=UDV^(t)  E9

[0194] where D is a 3×3 diagonal matrix and U and V are orthogonal 3×3matrices

[0195] Equation E10 further defines the rotational R description of thetransformation T in terms of U and V orthogonal 3×3 matrices as:

R=UV^(T)  E10

[0196] Equation E11 further defines the translation transform tdescription of the transformation T in terms of {overscore(p)},{overscore (q)} and R as:

t={overscore (p)}−R{overscore (q)}  E11

[0197] Equations E8 through E11 present a method to determine the rigidtransformation between two point sets p and q—this process correspondsto step 1152 in FIG. 17.

[0198] The steps of the registration algorithm are applied iterativelyuntil convergence. The iterative sequence includes a Find Closest Pointson Fixed Image 1148 step, a Determine New Transform 1152 step, aCalculate Distances 1156 step, and Converged decision 1160 step.

[0199] In the Find Closest Points on Fixed Image 1148 step,corresponding q points are found for each point in the fixed set p.Correspondence is defined by determining the closest edge point on q tothe edge point of p. The distance transform image helps locate theseclosest points. Once p and closest -q pixels are identified, theDetermine New Transform 1152 step calculates the rotation R via SVDanalysis using equations E8-E10 and translation transform t via equationE11. If, at decision step 1160, the change in the average closest pointdistance between two iterations is less than 5%, then the predicted-qpixel candidates are considered converged and suitable for receiving thetransforms R and t to rigidly register the moving image Transform 1136onto the common voxels p of the 3D Scan Converted 1108 image. At thispoint, the rigid registration process is complete as closest proximitybetween voxel or pixel sets has occurred between the fixed and movingimages, and the process continues with fusion at step 1016.

[0200] If, however, there is >5% change between the predicted-q pixelsand p pixels, another iteration cycle is applied via the Apply Transform1140 to the Find Closest Points on Fixed Image 1148 step, and is cycledthrough the converged 1160 decision block. Usually in 3 cycles, thoughas many as 20 iterative cycles, are engaged until is the transformationT is considered converged.

[0201] A representative example for the application of the preferredembodiment for the registration and fusion of a moving image onto afixed image is shown in FIGS. 17A-17C.

[0202]FIG. 17A is a first measurement view of a fixed scanplane 1200Afrom a 3D data set measurement taken at a first site. A first pixel setp consistent for the dark pixels of AFV is shown in a region 1204A. Theregion 1204A has approximate x-y coordinates of (150, 120) that isclosest to dark edge.

[0203]FIG. 17B is a second measurement view of a moving scanplane 1200Bfrom a 3D data set measurement taken at a second site. A second pixelset q consistent for the dark pixels of AFV is shown in a region 1204B.The region 1204B has approximate x-y coordinates of (50, 125) that isclosest to dark edge.

[0204]FIG. 17C is a composite image 1200C of the first (fixed) 1200A andsecond (moving) 1200B images in which common pixels 1204B at approximatecoordinates (50,125) is aligned or overlapped with the voxels 1204A atapproximate coordinates (150, 120). That is, the region 1204B pixel setq is linearly and rotational transformed consistent with the closestedge selection methodology as shown in FIGS. 13A and 13B from employingthe 3D Edge Detection 1112 step.. The composite image 1200C is a mosaicimage from scanplanes having approximately the same φ and rotation θangles.

[0205] The registration and fusing of common pixel sets p and q fromscanplanes having approximately the same φ and rotation θ angles can berepeated for other scanplanes in each 3D data set taken at the first(fixed) and second(moving) anatomical sites. For example, if thecomposite image 1200C above was for scanplane #1, then the process maybe repeated for the remaining scanplanes #2-24 or #2-48 or greater asneeded to capture a completed uterine mosaic image. Thus an arraysimilar to the 3D array 240 from FIG. 5B is assembled, except this timethe scanplane array is made of composite images, each composited imagebelonging to a scanplane having approximately the same φ and rotation θangles.

[0206] If a third and a fourth 3D data sets are taken, the respectiveregistration, fusing, and assembling into scanplane arrays of compositedimages is undertaken with the same procedures. In this case, thescanplane composite array similar to the 3D array 240 is composed of agreater mosaic number of registered and fused scanplane images.

[0207] A representative example the fusing of two moving images onto afixed image is shown in FIGS. 18A-18D.

[0208]FIG. 18A is a first view of a fixed scanplane 1220A. Region 1224Ais identified as p voxels approximately at the coordinates (150, 70).

[0209]FIG. 18B is a second view of a first moving scanplane 1220B havingsome q voxels 1224B at x-y coordinates (300, 100) common with the firstmeasurements p voxels at x-y coordinates (150, 70). Another set ofvoxels 1234A is shown roughly near the intersection of x-y coordinates(200, 125). As the transceiver 10 was moved only translationally, Thescanplane 1220B from the second site has approximately the same tilt φand rotation θ angles of the fixed scanplane 1220A taken from the firstlateral in-line site.

[0210]FIG. 18C is a third view of a moving scanplane 1220C. A region1234B is identified as q voxels approximately at the x-y coordinates(250, 100) that are common with the second views q voxels 1234A. Thescanplane 1220c from the third lateral in-line site has approximatelythe same tilt φ and rotation θ angles of the fixed scanplane 1220A takenfrom the first lateral in-line site and the first moving scanplane 1220Btaken from the second lateral in-line site.

[0211]FIG. 18D is a composite mosaic image 1220D of the first (fixed)1220A image the second (moving) 1220B image, and the third (moving)1220C image representing the sequential alignment and fusing of q voxelsets 1224B to 1224A, and 1234B with 1234A.

[0212] A fourth image similarly could be made to bring about a 4-imagemosaic from scanplanes from a fourth 3D data set acquired from thetransceiver 10 taking measurements at a fourth anatomical site where thefourth 3D data set is acquired with approximately the same tilt φ androtation θ angles.

[0213] The transceiver 10 is moved to different anatomical sites tocollect 3D data sets by hand placement by an operator. Such handplacement could create the acquiring of 3D data sets under conditions inwhich the tilt φ and rotation θ angles are not approximately equal, butdiffer enough to cause some measurement error requiring correction touse the rigid registration 1012 algorithm. In the event where the 3Ddata sets between anatomical sites, either between a moving supine sitein relation to its beginning fixed site, or between a moving lateralsite with its beginning fixed site, cannot be acquired with the tilt φand rotation θ angles being approximately the same, then the built-inaccelerometer measures the changes in tilt φ and rotation θ angles andcompensates accordingly so that acquired moving images are presented ifthough they were acquired under approximately equal tilt φ and rotationθ angle conditions.

[0214] Demonstrations of the algorithmic manipulation of pixels of thepresent invention are provided in Appendix 1: Examples of AlgorithmicSteps. Source code of the algorithms of the present invention isprovided in Appendix 2: Matlab Source Code.

[0215] While the preferred embodiment of the invention has beenillustrated and described, as noted above, many changes can be madewithout departing from the spirit and scope of the invention. Forexample, other uses of the invention include determining the areas andvolumes of the prostate, heart, bladder, and other organs and bodyregions of clinical interest. Accordingly, the scope of the invention isnot limited by the disclosure of the preferred embodiment

We claim:
 1. A method to determine amniotic fluid volume in digitalimages, the method comprising: positioning an ultrasound transceiver tosend and receive echoes from a portion of a uterus of a patient, thetransceiver adapted to form a plurality of scanplanes; enhancing theimages of the amniotic fluid regions in the scanplanes with a pluralityof algorithms; associating the scanplanes into an array, and determiningthe amniotic fluid volume of the amniotic fluid regions within thearray.
 2. The method of claim 1, wherein plurality of scanplanes areacquired from a rotational array, a translational array, or a wedgearray.
 3. The method of claim 1, wherein the plurality of algorithmsincludes algorithms for image enhancement, segmentation, and polishing.4. The method of claim 3, wherein segmentation further includes anintensity clustering step, a spatial gradients step, a hysteresisthreshold step, a Region-of-Interest selection step, and a matchingedges filter step.
 5. The method of claim 4, wherein the intensityclustering step is performed in a first parallel operation, and thespatial gradients, hysteresis threshold, Region-of-Interest selection,and matching edges filter steps are performed in a second paralleloperation, and further wherein the results from the first paralleloperation are combined with the results from the second paralleloperation.
 6. The method of claim 3, wherein image enhancement furtherincludes applying a heat filter and a shock filter to the digitalimages.
 7. The method of claim 6 wherein the heat filter is applied tothe digital images followed by application of the shock filter to thedigital images.
 8. The method of claim 1, wherein the amniotic fluidvolume is adjusted for underestimation or overestimation.
 9. The methodof claim 8, wherein the amniotic fluid volume is adjusted forunderestimation by probing with adjustable ultrasound frequencies topenetrate deep tissues and to repositioning the transceiver to establishthat deep tissues are exposed with probing ultrasound of sufficientstrength to provide a reflecting ultrasound echo receivable by thetransceiver, such that more than one rotational array to detect deeptissue and regions of the fetal head are obtained.
 10. The method ofclaim 8, wherein amniotic fluid volume is adjusted for overestimation byautomatically determining fetal head volume contribution to amnioticfluid volume and deducting it from the amniotic fluid volume.
 11. Themethod of claim 10, wherein the steps to adjust for overestimatedamniotic fluid volumes include a 2D clustering step, a matching edgesstep, an all edges step, a gestational age factor step, a head diameterstep, an head edge detection step, and a Hough transform step.
 12. Themethod of claim 12, wherein the Hough transform step includes a polarHough Transform step, a Find Maximum Hough value step, and a fill circleregion step.
 13. The method of claim 12, wherein the polar HoughTransform step includes a first Hough transform to look for lines of aspecified shape, and a second Hough transform to look for fetal headstructures.
 14. A method to determine the volume of structures indigital images, the method comprising: positioning an ultrasoundtransceiver exterior to a patient at a plurality of patient locationsorientated at approximately the same orientation such that at least aportion of the structure is within the range of the transceiver to sendand receive echoes from the portion of a structure; transmitting radiofrequency ultrasound pulses distributed as 2D scanplanes, each scanplanehaving a plurality of scanlines, to, and receiving those pulses echoedback from, the internal and external boundaries of the structure; and,based on those pulses a) enhancing the image of the structure in eachscanplane; b) forming a 3D array of scanplanes for each patientlocation; c) aligning a common portion of images in similarly orientatedscanplanes from adjacent 3D arrays; d) fusing the images from similarlyorientated scanplanes from adjacent 3D arrays and forming a mosaic 3Darray, and e) calculating the volume of the structure in the mosaic 3Darray.
 15. The method of claim 14, wherein the structure is a uterus,the internal boundary is between the amniotic fluid and tissue, theplurality of patient locations is four such that a first 3D array isobtained from a first transceiver location, a second 3D array isobtained from a second transceiver location, a third 3D array isobtained from a third transceiver location, and a fourth 3D array isobtained from a fourth transceiver location, each 3D array having thesimilarly orientated scanplanes of substantially the same φ and rotationθ angles.
 16. The method of claim 15, wherein the identification of thecommon portion of the amniotic fluid images is by selection of lowintensity pixels at the internal boundary along the amniotic fluid inscanplanes having substantially the same φ and rotation θ angles fromthe first, second, third, and fourth 3D arrays.
 17. The method of claim16, wherein the aligning and fusing of the common portion of images inscanplanes having substantially the same same φ and rotation θ anglesfrom the first and second 3D arrays comprises: a) determining andapplying a 3D distance transform that brings the low intensity pixels ofa second image to closest proximity to the low intensity pixels of afirst image that is common with the second image, and b) repeating step(a) as necessary to achieve closest proximity as shown to be a minimumchange in location of the low intensity pixels, then fusing the secondimage to the first image.
 18. The method of claim 17, wherein thealigning and fusing of the common portion of images in scanplanes havingsubstantially the same same φ and rotation θ angles from the second andthird 3D arrays comprises: a) determining and applying a 3D distancetransform that brings the low intensity pixels of a third image toclosest proximity to the low intensity pixels of the second image thatis common with the third image, and b) repeating step (a) as necessaryto achieve closest proximity as shown to be a minimum change in locationof the low intensity pixels, then fusing the third image to the secondimage.
 19. The method of claim 19, wherein the aligning and fusing ofcommon portion of images in scanplanes having substantially the samesame φ and rotation θ angles from the third and fourth 3D arrayscomprises: a) determining and applying a 3D distance transform thatbrings the low intensity pixels of a fourth image to closest proximityto the low intensity pixels of the third image that is common with thefourth image, and b) repeating step (a) as necessary to achieve closestproximity as shown to be a minimum change in location of the lowintensity pixels, then fusing the fourth image to the third image. 20.The method of claim 19, wherein the patient is in a supine position andthe 3D arrays are obtained from the corners of a substantiallyrectangular grid, each corner being approximately the midpoint of auterine quadrant.
 21. The method of claim 19, wherein the patient is ina lateral position and the 3D arrays are obtained along a line over theuterus, each 3D array being separated from the other by a measurableinterval.
 22. The method of claim 14, wherein the transceiver is furtheradapted to measure the tilt φ and rotation θ angles differences and tofurther adjust the tilt φ and rotation θ angles between locations to beapproximately the same.
 23. The method of claim 22, wherein the tilt φand rotation θ angles are measured by an accelerometer.
 24. A method todetermine the volume of structures in digital images, the methodcomprising: positioning an ultrasound transceiver exterior to a patientat a plurality of patient locations such that at least a portion of thestructure is within the range of the transciever to send and receiveechoes from a portion of a structure; transmitting radio frequencyultrasound pulses delivered as 3D-distributed scanlines to, andreceiving those pulses echoed back from, the internal and externalboundaries of the structure; and, based on those pulses a) forming athree-dimensional scancone for each patient location; b) enhancing thestructural image in each scancone; c) aligning a common portion of thestructure between adjacent scancones; d) fusing the aligned scancones toform a 3D mosaic image, and e) calculating the volume of the structurein the 3D mosaic image.
 25. The method of claim 24, wherein thestructure is a uterus, the internal boundary is between the amnioticfluid and tissue, the plurality of patient locations is four such that afirst scancone is obtained from a first transceiver location, a secondscancone is obtained from a second transceiver location, a thirdscancone is obtained from a third transceiver location, and a fourthscancone is obtained from a fourth transceiver location.
 26. The methodof claim 25, wherein the identification of the common portion of theamniotic fluid images is by selection of low intensity pixels at theinternal boundary along the amniotic fluid in the first, second, third,and fourth scancones.
 27. The method of claim 26, wherein the aligningand fusing of the common portion for the first and second scanconescomprises: a) determining and applying a 3D distance transform thatbrings the low intensity pixels of the second scancone to closestproximity to the low intensity pixels of the first scancone that iscommon with the second scancone, and b) repeating step (a) as necessaryto achieve closest proximity as shown to be a minimum change in locationof the low intensity pixels, then fusing the second scancone to thefirst scancone.
 28. The method of claim 27, wherein the aligning andfusing of the common portion for the second and third scanconescomprises: a) determining and applying a 3D distance transform thatbrings the low intensity pixels of the third scancone to closestproximity to the low intensity pixels of the second scancone that iscommon with the third scancone, and b) repeating step (a) as necessaryto achieve closest proximity as shown to be a minimum change in locationof the low intensity pixels, then fusing the third scancone to thesecond scancone.
 29. The method of claim 28, wherein the aligning andfusing of the common portion for the third and fourth scanconescomprises: a) determining and applying a 3D distance transform thatbrings the low intensity pixels of the fourth scancone to closestproximity to the low intensity pixels of the third scancone that iscommon with the fourth scancone, and b) repeating step (a) as necessaryto achieve closest proximity as shown to be a minimum change in locationof the low intensity pixels, then fusing the fourth scancone to thethird scancone.
 30. The method of claim 29, wherein the patient is in asupine position and the scancones are obtained from the corners of asubstantially rectangular grid, each corner being approximately themidpoint of a uterine quadrant.
 31. The method of claim 29, wherein thepatient is in a lateral position and the scancones are obtained along aline over the uterus, each scancone being separated from the other by ameasurable interval.
 32. A system for determining amniotic fluid volume,the system comprising: a transceiver positioned on at least one locationof a patient, the transceiver configured to deliver radio frequencyultrasound pulses to amniotic fluid regions of a patient, to receiveechoes of the pulses reflected from the amniotic fluid regions, toconvert the echoes to digital form, and to determine the tilt androtational orientations of the ultrasound pulses; a computer system incommunication with the transceiver, the computer system having amicroprocessor and a memory, the memory further containing storedprogramming instructions operable by the microprocessor to associate theplurality of scanplanes into a rotational array, and the memory furthercontaining instructions operable by the microprocessor to determine thepresence of an amniotic fluid region in each scanplane and determine theamniotic fluid volume spanning between and through each scanplane of therotational array.
 33. The system of claim 32, wherein tilt androtational orientations of the ultrasound pulses is determined by anaccelerometer in the transceiver, the accelerometer configured tomeasure differences in the angular positions of the transceiver.
 34. Thesystem of claim 32, wherein each scanplane is arranged as a plurality ofscanlines, each scanline of the plurality of scanlines being separatedby approximately 1.5 degrees and having a length suitable for thedimension of amniotic fluid region.
 35. The system of claim 34, whereinscanplanes have similar orientations from a plurality of transceiverlocations
 36. The system of claim 32, wherein the programminginstructions include a plurality of algorithms for image enhancement,segmentation, and polishing.
 37. The system of claim 36, wherein thesteps for image enhancement further include application of a heat filterfollowed by application of a shock filter.
 38. The system of claim 32,wherein the amniotic fluid volumes are adjusted for underestimation andoverestimation.
 39. The system of claim 38, wherein the amniotic fluidvolumes are adjusted for underestimation by probing with ultrasoundfrequencies having sufficient power and wavelength to penetrate throughfatty tissue to reach amniotic fluid regions and to provide detectableecho signals receivable to the transceiver to reveal amniotic fluidregions.
 40. The system of claim 39, wherein the amniotic fluid volumesare further adjusted for underestimation by repositioning thetransceiver to acquire more than one rotational array to detect deeptissue and regions of the fetal head.
 41. The system of claim 38,wherein the amniotic fluid volumes are adjusted for overestimation bydetecting the location of a fetal head, determining the volume of thefetal head, and deducting the volume of the fetal head from the amnioticfluid volume spanning between and through each scanplane of therotational array.
 42. The system of claim 32, wherein the computersystem is configured for remote operation via an Internet web-basedsystem, the internet web-based system having a plurality of programsthat collect, analyze, and store amniotic fluid volume.